qFit-ligand Reveals Widespread Conformational Heterogeneity of Drug-Like Molecules in X‑Ray Electron Density Maps

Proteins and ligands sample a conformational ensemble that governs molecular recognition, activity, and dissociation. In structure-based drug design, access to this conformational ensemble is critical to understand the balance between entropy and enthalpy in lead optimization. However, ligand confor...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of medicinal chemistry 2018-12, Vol.61 (24), p.11183-11198
Hauptverfasser: van Zundert, Gydo C. P, Hudson, Brandi M, de Oliveira, Saulo H. P, Keedy, Daniel A, Fonseca, Rasmus, Heliou, Amelie, Suresh, Pooja, Borrelli, Kenneth, Day, Tyler, Fraser, James S, van den Bedem, Henry
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 11198
container_issue 24
container_start_page 11183
container_title Journal of medicinal chemistry
container_volume 61
creator van Zundert, Gydo C. P
Hudson, Brandi M
de Oliveira, Saulo H. P
Keedy, Daniel A
Fonseca, Rasmus
Heliou, Amelie
Suresh, Pooja
Borrelli, Kenneth
Day, Tyler
Fraser, James S
van den Bedem, Henry
description Proteins and ligands sample a conformational ensemble that governs molecular recognition, activity, and dissociation. In structure-based drug design, access to this conformational ensemble is critical to understand the balance between entropy and enthalpy in lead optimization. However, ligand conformational heterogeneity is currently severely underreported in crystal structures in the Protein Data Bank, owing in part to a lack of automated and unbiased procedures to model an ensemble of protein–ligand states into X-ray data. Here, we designed a computational method, qFit-ligand, to automatically resolve conformationally averaged ligand heterogeneity in crystal structures, and applied it to a large set of protein receptor–ligand complexes. In an analysis of the cancer related BRD4 domain, we found that up to 29% of protein crystal structures bound with drug-like molecules present evidence of unmodeled, averaged, relatively isoenergetic conformations in ligand–receptor interactions. In many retrospective cases, these alternate conformations were adventitiously exploited to guide compound design, resulting in improved potency or selectivity. Combining qFit-ligand with high-throughput screening or multitemperature crystallography could therefore augment the structure-based drug design toolbox.
doi_str_mv 10.1021/acs.jmedchem.8b01292
format Article
fullrecord <record><control><sourceid>acs_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6820680</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>b149982669</sourcerecordid><originalsourceid>FETCH-LOGICAL-a510t-b7675a228d2ea2bda20ad778c49e44806b148928e21940ec02d25984d43dd30c3</originalsourceid><addsrcrecordid>eNp9kcFuEzEURS0EoqHwBwhZ7FhMePZ4ZjwbpCptCVIqpAoEO8tjvyQuEzu1J5Gy4xf4Rb4Eh2krYMHK0vO513o-hLxkMGXA2Vtt0vRmg9ascTOVHTDe8kdkwioOhZAgHpMJAOcFr3l5Qp6ldAMAJePlU3JSgqgaWckJSbeXbih6t9Le0mvco-4T_eIspm1Ebeks-GWIGz244HVP5zhgDCv06IYDDUt6HnerYuG-Ib0KPZpdj4k6T7_-_P7jWh_oRZ4NMXh6jj4dI1d6m56TJ8v8DL64O0_J58uLT7N5sfj4_sPsbFHoisFQdE3dVJpzaTlq3lnNQdumkUa0KPKGdceEbLlEzloBaIBbXrVSWFFaW4IpT8m7sXe7644fhX6Iulfb6DY6HlTQTv19491arcJe1ZJDLSEXvB4LQhqcSsYNaNYmeJ-XUky0ZdXUGXozQut_uudnC3WcgRB1DbLZs8yKkTUxpBRx-RBgoI5WVbaq7q2qO6s59urPRR5C9xozACPwOx52MbtK_-_8BbdFs3E</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>qFit-ligand Reveals Widespread Conformational Heterogeneity of Drug-Like Molecules in X‑Ray Electron Density Maps</title><source>MEDLINE</source><source>ACS Publications</source><creator>van Zundert, Gydo C. P ; Hudson, Brandi M ; de Oliveira, Saulo H. P ; Keedy, Daniel A ; Fonseca, Rasmus ; Heliou, Amelie ; Suresh, Pooja ; Borrelli, Kenneth ; Day, Tyler ; Fraser, James S ; van den Bedem, Henry</creator><creatorcontrib>van Zundert, Gydo C. P ; Hudson, Brandi M ; de Oliveira, Saulo H. P ; Keedy, Daniel A ; Fonseca, Rasmus ; Heliou, Amelie ; Suresh, Pooja ; Borrelli, Kenneth ; Day, Tyler ; Fraser, James S ; van den Bedem, Henry ; SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)</creatorcontrib><description>Proteins and ligands sample a conformational ensemble that governs molecular recognition, activity, and dissociation. In structure-based drug design, access to this conformational ensemble is critical to understand the balance between entropy and enthalpy in lead optimization. However, ligand conformational heterogeneity is currently severely underreported in crystal structures in the Protein Data Bank, owing in part to a lack of automated and unbiased procedures to model an ensemble of protein–ligand states into X-ray data. Here, we designed a computational method, qFit-ligand, to automatically resolve conformationally averaged ligand heterogeneity in crystal structures, and applied it to a large set of protein receptor–ligand complexes. In an analysis of the cancer related BRD4 domain, we found that up to 29% of protein crystal structures bound with drug-like molecules present evidence of unmodeled, averaged, relatively isoenergetic conformations in ligand–receptor interactions. In many retrospective cases, these alternate conformations were adventitiously exploited to guide compound design, resulting in improved potency or selectivity. Combining qFit-ligand with high-throughput screening or multitemperature crystallography could therefore augment the structure-based drug design toolbox.</description><identifier>ISSN: 0022-2623</identifier><identifier>EISSN: 1520-4804</identifier><identifier>DOI: 10.1021/acs.jmedchem.8b01292</identifier><identifier>PMID: 30457858</identifier><language>eng</language><publisher>United States: American Chemical Society</publisher><subject>Algorithms ; Amyloid Precursor Protein Secretases - antagonists &amp; inhibitors ; Amyloid Precursor Protein Secretases - chemistry ; Amyloid Precursor Protein Secretases - metabolism ; Aspartic Acid Endopeptidases - antagonists &amp; inhibitors ; Aspartic Acid Endopeptidases - chemistry ; Aspartic Acid Endopeptidases - metabolism ; Bioinformatics ; Calibration ; Cell Cycle Proteins ; Computational Biology - methods ; Computer Science ; Crystallography, X-Ray ; Databases, Protein ; Drug Design ; Electrons ; High-Throughput Screening Assays - methods ; INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY ; Ligands ; Models, Molecular ; Nuclear Proteins - chemistry ; Protein Domains ; Proteins - chemistry ; Proteins - metabolism ; Transcription Factors - chemistry</subject><ispartof>Journal of medicinal chemistry, 2018-12, Vol.61 (24), p.11183-11198</ispartof><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a510t-b7675a228d2ea2bda20ad778c49e44806b148928e21940ec02d25984d43dd30c3</citedby><cites>FETCH-LOGICAL-a510t-b7675a228d2ea2bda20ad778c49e44806b148928e21940ec02d25984d43dd30c3</cites><orcidid>0000-0003-2288-9157 ; 0000-0003-2358-841X ; 000000032358841X ; 0000000322889157</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://pubs.acs.org/doi/pdf/10.1021/acs.jmedchem.8b01292$$EPDF$$P50$$Gacs$$H</linktopdf><linktohtml>$$Uhttps://pubs.acs.org/doi/10.1021/acs.jmedchem.8b01292$$EHTML$$P50$$Gacs$$H</linktohtml><link.rule.ids>230,315,781,785,886,2766,27081,27929,27930,56743,56793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30457858$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-04466087$$DView record in HAL$$Hfree_for_read</backlink><backlink>$$Uhttps://www.osti.gov/servlets/purl/1493576$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>van Zundert, Gydo C. P</creatorcontrib><creatorcontrib>Hudson, Brandi M</creatorcontrib><creatorcontrib>de Oliveira, Saulo H. P</creatorcontrib><creatorcontrib>Keedy, Daniel A</creatorcontrib><creatorcontrib>Fonseca, Rasmus</creatorcontrib><creatorcontrib>Heliou, Amelie</creatorcontrib><creatorcontrib>Suresh, Pooja</creatorcontrib><creatorcontrib>Borrelli, Kenneth</creatorcontrib><creatorcontrib>Day, Tyler</creatorcontrib><creatorcontrib>Fraser, James S</creatorcontrib><creatorcontrib>van den Bedem, Henry</creatorcontrib><creatorcontrib>SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)</creatorcontrib><title>qFit-ligand Reveals Widespread Conformational Heterogeneity of Drug-Like Molecules in X‑Ray Electron Density Maps</title><title>Journal of medicinal chemistry</title><addtitle>J. Med. Chem</addtitle><description>Proteins and ligands sample a conformational ensemble that governs molecular recognition, activity, and dissociation. In structure-based drug design, access to this conformational ensemble is critical to understand the balance between entropy and enthalpy in lead optimization. However, ligand conformational heterogeneity is currently severely underreported in crystal structures in the Protein Data Bank, owing in part to a lack of automated and unbiased procedures to model an ensemble of protein–ligand states into X-ray data. Here, we designed a computational method, qFit-ligand, to automatically resolve conformationally averaged ligand heterogeneity in crystal structures, and applied it to a large set of protein receptor–ligand complexes. In an analysis of the cancer related BRD4 domain, we found that up to 29% of protein crystal structures bound with drug-like molecules present evidence of unmodeled, averaged, relatively isoenergetic conformations in ligand–receptor interactions. In many retrospective cases, these alternate conformations were adventitiously exploited to guide compound design, resulting in improved potency or selectivity. Combining qFit-ligand with high-throughput screening or multitemperature crystallography could therefore augment the structure-based drug design toolbox.</description><subject>Algorithms</subject><subject>Amyloid Precursor Protein Secretases - antagonists &amp; inhibitors</subject><subject>Amyloid Precursor Protein Secretases - chemistry</subject><subject>Amyloid Precursor Protein Secretases - metabolism</subject><subject>Aspartic Acid Endopeptidases - antagonists &amp; inhibitors</subject><subject>Aspartic Acid Endopeptidases - chemistry</subject><subject>Aspartic Acid Endopeptidases - metabolism</subject><subject>Bioinformatics</subject><subject>Calibration</subject><subject>Cell Cycle Proteins</subject><subject>Computational Biology - methods</subject><subject>Computer Science</subject><subject>Crystallography, X-Ray</subject><subject>Databases, Protein</subject><subject>Drug Design</subject><subject>Electrons</subject><subject>High-Throughput Screening Assays - methods</subject><subject>INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY</subject><subject>Ligands</subject><subject>Models, Molecular</subject><subject>Nuclear Proteins - chemistry</subject><subject>Protein Domains</subject><subject>Proteins - chemistry</subject><subject>Proteins - metabolism</subject><subject>Transcription Factors - chemistry</subject><issn>0022-2623</issn><issn>1520-4804</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kcFuEzEURS0EoqHwBwhZ7FhMePZ4ZjwbpCptCVIqpAoEO8tjvyQuEzu1J5Gy4xf4Rb4Eh2krYMHK0vO513o-hLxkMGXA2Vtt0vRmg9ascTOVHTDe8kdkwioOhZAgHpMJAOcFr3l5Qp6ldAMAJePlU3JSgqgaWckJSbeXbih6t9Le0mvco-4T_eIspm1Ebeks-GWIGz244HVP5zhgDCv06IYDDUt6HnerYuG-Ib0KPZpdj4k6T7_-_P7jWh_oRZ4NMXh6jj4dI1d6m56TJ8v8DL64O0_J58uLT7N5sfj4_sPsbFHoisFQdE3dVJpzaTlq3lnNQdumkUa0KPKGdceEbLlEzloBaIBbXrVSWFFaW4IpT8m7sXe7644fhX6Iulfb6DY6HlTQTv19491arcJe1ZJDLSEXvB4LQhqcSsYNaNYmeJ-XUky0ZdXUGXozQut_uudnC3WcgRB1DbLZs8yKkTUxpBRx-RBgoI5WVbaq7q2qO6s59urPRR5C9xozACPwOx52MbtK_-_8BbdFs3E</recordid><startdate>20181227</startdate><enddate>20181227</enddate><creator>van Zundert, Gydo C. P</creator><creator>Hudson, Brandi M</creator><creator>de Oliveira, Saulo H. P</creator><creator>Keedy, Daniel A</creator><creator>Fonseca, Rasmus</creator><creator>Heliou, Amelie</creator><creator>Suresh, Pooja</creator><creator>Borrelli, Kenneth</creator><creator>Day, Tyler</creator><creator>Fraser, James S</creator><creator>van den Bedem, Henry</creator><general>American Chemical Society</general><general>American Chemical Society (ACS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>1XC</scope><scope>OIOZB</scope><scope>OTOTI</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-2288-9157</orcidid><orcidid>https://orcid.org/0000-0003-2358-841X</orcidid><orcidid>https://orcid.org/000000032358841X</orcidid><orcidid>https://orcid.org/0000000322889157</orcidid></search><sort><creationdate>20181227</creationdate><title>qFit-ligand Reveals Widespread Conformational Heterogeneity of Drug-Like Molecules in X‑Ray Electron Density Maps</title><author>van Zundert, Gydo C. P ; Hudson, Brandi M ; de Oliveira, Saulo H. P ; Keedy, Daniel A ; Fonseca, Rasmus ; Heliou, Amelie ; Suresh, Pooja ; Borrelli, Kenneth ; Day, Tyler ; Fraser, James S ; van den Bedem, Henry</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a510t-b7675a228d2ea2bda20ad778c49e44806b148928e21940ec02d25984d43dd30c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Algorithms</topic><topic>Amyloid Precursor Protein Secretases - antagonists &amp; inhibitors</topic><topic>Amyloid Precursor Protein Secretases - chemistry</topic><topic>Amyloid Precursor Protein Secretases - metabolism</topic><topic>Aspartic Acid Endopeptidases - antagonists &amp; inhibitors</topic><topic>Aspartic Acid Endopeptidases - chemistry</topic><topic>Aspartic Acid Endopeptidases - metabolism</topic><topic>Bioinformatics</topic><topic>Calibration</topic><topic>Cell Cycle Proteins</topic><topic>Computational Biology - methods</topic><topic>Computer Science</topic><topic>Crystallography, X-Ray</topic><topic>Databases, Protein</topic><topic>Drug Design</topic><topic>Electrons</topic><topic>High-Throughput Screening Assays - methods</topic><topic>INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY</topic><topic>Ligands</topic><topic>Models, Molecular</topic><topic>Nuclear Proteins - chemistry</topic><topic>Protein Domains</topic><topic>Proteins - chemistry</topic><topic>Proteins - metabolism</topic><topic>Transcription Factors - chemistry</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>van Zundert, Gydo C. P</creatorcontrib><creatorcontrib>Hudson, Brandi M</creatorcontrib><creatorcontrib>de Oliveira, Saulo H. P</creatorcontrib><creatorcontrib>Keedy, Daniel A</creatorcontrib><creatorcontrib>Fonseca, Rasmus</creatorcontrib><creatorcontrib>Heliou, Amelie</creatorcontrib><creatorcontrib>Suresh, Pooja</creatorcontrib><creatorcontrib>Borrelli, Kenneth</creatorcontrib><creatorcontrib>Day, Tyler</creatorcontrib><creatorcontrib>Fraser, James S</creatorcontrib><creatorcontrib>van den Bedem, Henry</creatorcontrib><creatorcontrib>SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>OSTI.GOV - Hybrid</collection><collection>OSTI.GOV</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of medicinal chemistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>van Zundert, Gydo C. P</au><au>Hudson, Brandi M</au><au>de Oliveira, Saulo H. P</au><au>Keedy, Daniel A</au><au>Fonseca, Rasmus</au><au>Heliou, Amelie</au><au>Suresh, Pooja</au><au>Borrelli, Kenneth</au><au>Day, Tyler</au><au>Fraser, James S</au><au>van den Bedem, Henry</au><aucorp>SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>qFit-ligand Reveals Widespread Conformational Heterogeneity of Drug-Like Molecules in X‑Ray Electron Density Maps</atitle><jtitle>Journal of medicinal chemistry</jtitle><addtitle>J. Med. Chem</addtitle><date>2018-12-27</date><risdate>2018</risdate><volume>61</volume><issue>24</issue><spage>11183</spage><epage>11198</epage><pages>11183-11198</pages><issn>0022-2623</issn><eissn>1520-4804</eissn><abstract>Proteins and ligands sample a conformational ensemble that governs molecular recognition, activity, and dissociation. In structure-based drug design, access to this conformational ensemble is critical to understand the balance between entropy and enthalpy in lead optimization. However, ligand conformational heterogeneity is currently severely underreported in crystal structures in the Protein Data Bank, owing in part to a lack of automated and unbiased procedures to model an ensemble of protein–ligand states into X-ray data. Here, we designed a computational method, qFit-ligand, to automatically resolve conformationally averaged ligand heterogeneity in crystal structures, and applied it to a large set of protein receptor–ligand complexes. In an analysis of the cancer related BRD4 domain, we found that up to 29% of protein crystal structures bound with drug-like molecules present evidence of unmodeled, averaged, relatively isoenergetic conformations in ligand–receptor interactions. In many retrospective cases, these alternate conformations were adventitiously exploited to guide compound design, resulting in improved potency or selectivity. Combining qFit-ligand with high-throughput screening or multitemperature crystallography could therefore augment the structure-based drug design toolbox.</abstract><cop>United States</cop><pub>American Chemical Society</pub><pmid>30457858</pmid><doi>10.1021/acs.jmedchem.8b01292</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0003-2288-9157</orcidid><orcidid>https://orcid.org/0000-0003-2358-841X</orcidid><orcidid>https://orcid.org/000000032358841X</orcidid><orcidid>https://orcid.org/0000000322889157</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0022-2623
ispartof Journal of medicinal chemistry, 2018-12, Vol.61 (24), p.11183-11198
issn 0022-2623
1520-4804
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6820680
source MEDLINE; ACS Publications
subjects Algorithms
Amyloid Precursor Protein Secretases - antagonists & inhibitors
Amyloid Precursor Protein Secretases - chemistry
Amyloid Precursor Protein Secretases - metabolism
Aspartic Acid Endopeptidases - antagonists & inhibitors
Aspartic Acid Endopeptidases - chemistry
Aspartic Acid Endopeptidases - metabolism
Bioinformatics
Calibration
Cell Cycle Proteins
Computational Biology - methods
Computer Science
Crystallography, X-Ray
Databases, Protein
Drug Design
Electrons
High-Throughput Screening Assays - methods
INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Ligands
Models, Molecular
Nuclear Proteins - chemistry
Protein Domains
Proteins - chemistry
Proteins - metabolism
Transcription Factors - chemistry
title qFit-ligand Reveals Widespread Conformational Heterogeneity of Drug-Like Molecules in X‑Ray Electron Density Maps
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-15T04%3A45%3A20IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-acs_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=qFit-ligand%20Reveals%20Widespread%20Conformational%20Heterogeneity%20of%20Drug-Like%20Molecules%20in%20X%E2%80%91Ray%20Electron%20Density%20Maps&rft.jtitle=Journal%20of%20medicinal%20chemistry&rft.au=van%20Zundert,%20Gydo%20C.%20P&rft.aucorp=SLAC%20National%20Accelerator%20Laboratory%20(SLAC),%20Menlo%20Park,%20CA%20(United%20States)&rft.date=2018-12-27&rft.volume=61&rft.issue=24&rft.spage=11183&rft.epage=11198&rft.pages=11183-11198&rft.issn=0022-2623&rft.eissn=1520-4804&rft_id=info:doi/10.1021/acs.jmedchem.8b01292&rft_dat=%3Cacs_pubme%3Eb149982669%3C/acs_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/30457858&rfr_iscdi=true